How to use LLM plugin in grafana? I just found auo-generate title description.Any other advance function?
The Grafana LLM plugin offers a variety of advanced features beyond just auto-generating titles and descriptions for dashboards and panels. Here are some of the key functionalities you can explore:
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Dashboard Save Changes Summary: The LLM plugin can generate a summary of the changes you've made to a dashboard when you save it. This helps in tracking the history of modifications efficiently.
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AI-Powered Flamegraph Interpretation: This feature uses AI to help you understand profiling data by identifying bottlenecks, root causes, and suggesting fixes. It's particularly useful for performance analysis.
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Incident Auto-Summary: The plugin can automatically summarize incidents, which is useful for alerting and incident response management.
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Sift Investigations: This feature helps identify error patterns in logs, Kubernetes crashes, and more, providing insights into potential issues.
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Custom LLM Provider Support: You can configure the plugin to use different LLM providers, such as OpenAI, Azure OpenAI, Anthropic, or even custom APIs with OpenAI-like signatures.
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Real-Time Streaming Interactions: The plugin supports live streaming of responses from the LLM provider, allowing for real-time interactions on the Grafana front end.
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Integration with Grafana MCP: The Model Context Protocol (MCP) allows for more complex interactions and actions as part of agent-like conversations with the LLM.
To enable these features, you need to install and configure the Grafana LLM app plugin. This involves setting up API access with your chosen LLM provider and enabling the necessary features in your Grafana instance. For more detailed instructions, you can refer to the Grafana LLM plugin documentation.
If you're developing plugins, you can also use the @grafana/llm npm package to integrate LLM functionality into your custom Grafana plugins. This allows you to make secure requests to the LLM and handle responses within your plugin's logic.
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I think the function is fewer. Did not have auto-generate graph function?
The Grafana LLM plugin currently does not support auto-generating graphs directly. Its primary focus is on enhancing the user experience with AI-powered features such as:
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Auto-generating Titles and Descriptions: This helps in quickly setting up dashboards by providing meaningful titles and descriptions based on the data.
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Dashboard Save Changes Summary: Automatically generates a summary of changes made to dashboards, aiding in version tracking and collaboration.
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AI-Powered Flamegraph Interpretation: Assists in understanding profiling data by identifying performance bottlenecks and suggesting fixes.
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Incident Auto-Summary: Summarizes incidents for better incident management and response.
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Sift Investigations: Analyzes logs to identify error patterns and potential issues.
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Custom LLM Provider Support: Allows integration with various LLM providers, offering flexibility in choosing the AI service.
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Real-Time Streaming Interactions: Provides live streaming of responses from LLM providers for real-time data interaction.
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Integration with Grafana MCP: Enables more complex interactions and actions through the Model Context Protocol.
While the plugin does not auto-generate graphs, it significantly enhances the analytical and descriptive capabilities of Grafana dashboards. If you're looking for more advanced data visualization features, you might need to explore other plugins or custom solutions within Grafana. For more detailed information on setting up and using these features, you can refer to the Grafana LLM plugin documentation.
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I test the garafna mcp server. It’s pefermance is not good. LLM plugin is for developer. Grafana AI assistant can do something better.
It seems like you're exploring the capabilities of the Grafana LLM plugin and looking for more advanced functionalities beyond auto-generating titles and descriptions. While the LLM plugin does not directly support auto-generating graphs, it offers several other advanced features that can enhance your Grafana experience:
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Dashboard Save Changes Summary: Automatically generates a summary of changes made to dashboards, which is useful for tracking modifications over time.
-
AI-Powered Flamegraph Interpretation: Helps in understanding profiling data by identifying performance bottlenecks and suggesting fixes, which is particularly useful for performance analysis.
-
Incident Auto-Summary: Automatically summarizes incidents, aiding in alerting and incident response management.
-
Sift Investigations: Analyzes logs to identify error patterns and potential issues, providing insights into system behavior.
-
Custom LLM Provider Support: Allows integration with various LLM providers, such as OpenAI, Azure OpenAI, Anthropic, or custom APIs, offering flexibility in choosing the AI service.
-
Real-Time Streaming Interactions: Supports live streaming of responses from LLM providers, enabling real-time data interaction on the Grafana front end.
-
Integration with Grafana MCP: The Model Context Protocol (MCP) allows for more complex interactions and actions as part of agent-like conversations with the LLM.
If you're interested in developing plugins, you can use the @grafana/llm npm package to integrate LLM functionality into your custom Grafana plugins. This allows you to make secure requests to the LLM and handle responses within your plugin's logic.
For more detailed information on setting up and using these features, you can refer to the Grafana LLM plugin documentation.
If you have any specific use cases or need further assistance, feel free to ask!
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did you find out how to use it me too I only see the auto-generate title and description and it doesn’t work always maybe becasue of the model i am using ollama ; llama3:8b
It seems like you're encountering some challenges with the Grafana LLM plugin, particularly with the auto-generate feature for titles and descriptions. Here are a few things to consider and try:
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Model Compatibility: The auto-generate feature's effectiveness can depend on the LLM model you are using. If you're using a custom model like
ollamaorllama3:8b, ensure that it is properly configured and compatible with the plugin's requirements. You might want to test with a different model, such as one from OpenAI or Azure OpenAI, to see if the issue persists. -
Plugin Configuration: Double-check that the LLM plugin is correctly configured in your Grafana instance. This includes ensuring that API keys are correctly set up and that the plugin is enabled. You can refer to the Grafana LLM plugin documentation for detailed setup instructions.
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Feature Limitations: The LLM plugin is primarily designed to enhance descriptive and analytical capabilities rather than generating visualizations like graphs. If you're looking for more advanced visualization features, you might need to explore other plugins or custom solutions within Grafana.
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Feedback and Support: If the auto-generate feature is not working as expected, consider providing feedback to Grafana Labs. They are continuously improving their tools and your input can be valuable. Additionally, you can reach out to the community or support for further assistance.
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Exploring Other Features: Besides auto-generating titles and descriptions, the LLM plugin offers other functionalities like incident auto-summary, AI-powered flamegraph interpretation, and more. Exploring these features might provide additional value to your Grafana setup.
If you have any specific questions or need further assistance, feel free to ask!
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Hi Team, I’ve installed the LLM app plugin in opensource grafana but I can’t see how can we use it as “real-time data interaction on the Grafana front end.” Can anyone suggest it?
Thanks.
Hi @nitindhiman,
The Grafana LLM plugin provides several advanced features, including real-time data interaction on the Grafana front end. Here's how you can leverage this functionality:
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Real-Time Streaming Interactions: The plugin supports live streaming of responses from LLM providers. This means you can have real-time interactions with the LLM, which can be particularly useful for dynamic data analysis and monitoring.
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Configuration: Ensure that your LLM plugin is correctly configured. This involves setting up API access with your chosen LLM provider. You can use providers like OpenAI, Azure OpenAI, or even custom APIs with OpenAI-like signatures. Make sure your API keys are correctly set up and that the plugin is enabled in your Grafana instance.
-
Integration with Grafana MCP: The Model Context Protocol (MCP) allows for more complex interactions and actions as part of agent-like conversations with the LLM. This can enhance the real-time capabilities of your Grafana setup.
-
Exploring Other Features: Besides real-time interactions, the LLM plugin offers functionalities like incident auto-summary, AI-powered flamegraph interpretation, and more. These features can provide additional value to your Grafana setup.
If you're developing plugins, you can use the @grafana/llm npm package to integrate LLM functionality into your custom Grafana plugins. This allows you to make secure requests to the LLM and handle responses within your plugin's logic.
For more detailed information on setting up and using these features, you can refer to the Grafana LLM plugin documentation.
If you have any specific questions or need further assistance, feel free to ask!
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how can we use it as “real-time data interaction on the Grafana Opensource front end?
Hi @nitindhiman,
To achieve real-time data interaction on the Grafana Open Source front end using the LLM plugin, you need to ensure a few key configurations and understand the capabilities of the plugin:
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Real-Time Streaming Interactions: The LLM plugin supports live streaming of responses from LLM providers. This feature allows you to interact with the LLM in real-time, which can be particularly useful for dynamic data analysis and monitoring. Ensure that your LLM provider supports streaming responses.
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Plugin Configuration: Make sure that the LLM plugin is correctly configured in your Grafana instance. This involves:
- Setting up API access with your chosen LLM provider. You can use providers like OpenAI, Azure OpenAI, or custom APIs with OpenAI-like signatures.
-
Ensuring that your API keys are correctly set up and that the plugin is enabled.
-
Integration with Grafana MCP: The Model Context Protocol (MCP) allows for more complex interactions and actions as part of agent-like conversations with the LLM. This can enhance the real-time capabilities of your Grafana setup by allowing the LLM to perform actions and gather information dynamically.
-
Exploring Other Features: Besides real-time interactions, the LLM plugin offers functionalities like incident auto-summary, AI-powered flamegraph interpretation, and more. These features can provide additional value to your Grafana setup.
-
Developing Custom Plugins: If you're developing plugins, you can use the
@grafana/llmnpm package to integrate LLM functionality into your custom Grafana plugins. This allows you to make secure requests to the LLM and handle responses within your plugin's logic.
For more detailed information on setting up and using these features, you can refer to the Grafana LLM plugin documentation.
If you have any specific questions or need further assistance, feel free to ask!
grot is still learning and improving, please let me know how I did by reacting below.